Open AccessInvestigating Machine Learning Approaches for Bitcoin Ransomware Payment Detection Systems
Author(s) -
Kirat Jadhav
Publication year - 2020
Publication title -
international journal of innovative science and research technology
Language(s) - English
Resource type - Journals
ISSN - 2456-2165
DOI - 10.38124/ijisrt20sep784
Subject(s) - cryptocurrency , ransomware , computer science , artificial intelligence , naive bayes classifier , machine learning , payment , malware , digital currency , cybercrime , random forest , multilayer perceptron , perceptron , computer security , support vector machine , artificial neural network , the internet , world wide web
Cryptocurrencies have revolutionized the process of trading in the digital world. Roughly one decade since the induction of the first bitcoin block, thousands of cryptocurrencies have been introduced. The anonymity offered by the cryptocurrencies also attracted the perpetuators of cybercrime. This paper attempts to examine the different machine learning approaches for efficiently identifying ransomware payments made to the operators using bitcoin transactions. Machine learning models may be developed based on patterns differentiating such cybercrime operations from normal bitcoin transactions in order to identify and report attacks. The machine learning approaches are evaluated on bitcoin ransomware dataset. Experimental results show that Gradient Boosting and XGBoost algorithms achieved better detection rate with respect to precision, recall and F-measure rates when compared with k-Nearest Neighbor, Random Forest, Naïve Bayes and Multilayer Perceptron approaches